Finally, another recent and very powerful class of ML models involves GANs. [1]}. These models consist of two neural networks, usually CNNs [2]}, the generator (G) and the discriminator (D). G tries to model the distribution of the real (target) data, eg. an image of a human face, by producing fake images, while D tries to discriminate between these and the real images that are provided to it. Based on this adversarial game, a trained G can produce artificial/synthetic data that are almost indistinguishable from the real ones. Whereas the inception of GANs envisaged them in an unsupervised setting, they have been adapted accordingly for semi-supervised learning by providing D with labeled data. Moreover, a useful type of GANs for propagation modeling is conditional GANs (cGANs) [3]}. These can include constraints on the data produced by G. GANs' generative ability is highly utilized in propagation modeling for dataset augmentation [4]}, [5]} or to improve the reliability of ML models when the number of labeled data is small [6]}. Finally, apart from GANs, other generative networks can also be used, such as deep belief networks (DBN) [7]}.
